English

Lossless Representation of Graphs using Distributions

Combinatorics 2007-10-11 v1 Computer Vision and Pattern Recognition

Abstract

We consider complete graphs with edge weights and/or node weights taking values in some set. In the first part of this paper, we show that a large number of graphs are completely determined, up to isomorphism, by the distribution of their sub-triangles. In the second part, we propose graph representations in terms of one-dimensional distributions (e.g., distribution of the node weights, sum of adjacent weights, etc.). For the case when the weights of the graph are real-valued vectors, we show that all graphs, except for a set of measure zero, are uniquely determined, up to isomorphism, from these distributions. The motivating application for this paper is the problem of browsing through large sets of graphs.

Keywords

Cite

@article{arxiv.0710.1870,
  title  = {Lossless Representation of Graphs using Distributions},
  author = {Mireille Boutin and Gregor Kemper},
  journal= {arXiv preprint arXiv:0710.1870},
  year   = {2007}
}

Comments

19 pages

R2 v1 2026-06-21T09:29:20.771Z